load libraries
library("tidyverse")
library("plyr")
library("dplyr")
library("ggplot2")
library("RColorBrewer")
library("data.table")
library("stringr")
library("janitor")
library("knitr")
library("kableExtra")
library("plotly")
knitr::opts_chunk$set(echo = TRUE, warning = FALSE, message = FALSE)
# fig.width = 20,
# fig.asp = 0.6,
# out.width = "100%")load data
data <- read.csv("/Users/nuriteliash/Documents/GitHub/varroa_Ploidy/data/Ploidy.csv") %>%
dplyr::mutate(Family = as.character(Family)) %>%
dplyr::mutate(stage_mature = case_when(
grepl("adult", Stage) ~ "Mature",
!grepl("adult", Stage) ~ "Imature"))
# order the levels
data$body.part <- factor(data$body.part, level=c("Body", "Anterior", "Posterior", "Legs","Hemolymph","Ovary","Testes"))
data$Stage <- factor(data$Stage, level=c("Larvae", "Protonymph", "Deuteronymph", "adult"))
data$Stage_original <- factor(data$Stage_original, level=c("Mom", "Son", "Mature","Daughter", "Deuteronymph", "Protonymph", "Larvae", "Immature"))
levels(data$Family) <- c(levels(data$Family), 0)
data$Family <- factor(data$Family, level= c("0", "1", "11","27","3","2","4", "5"))
p_family_adults = data %>% dplyr::filter(body.part %in% c("Body", "Ovary","Testes")) %>%
dplyr::filter(Stage == "adult") %>%
mutate_at("Family", ~replace_na(.,"0")) %>%
ggplot(aes(y=Ploidy, x=Sex, fill = Sex, lable = Stage)) +
geom_boxplot(outlier.shape = NA, coef=0 ) + theme_classic() + geom_jitter(width=0.1, size=2) +
facet_wrap(~body.part ) +
theme(axis.title.x = element_blank(),
axis.text.x = element_blank(),
axis.ticks = element_blank(),
panel.border=element_rect(colour="black",size=1, fill = NA))+
ggtitle('Mite ploidy in differnet body parts') + ylim(0, 3)
p_fam_body = data %>% dplyr::filter(body.part == "Body") %>%
dplyr::filter(Stage %in% c("Larvae", "Protonymph", "Deuteronymph", "adult")) %>%
ggplot(aes(y=Ploidy, x=Sex, fill = Sex, lable = Stage)) +
geom_boxplot(outlier.shape = NA, coef=0 ) + theme_classic() + geom_jitter(width=0.1, size=2) +
facet_wrap(~Stage, nrow = 1 ) +
ggtitle('Mite Ploidy in whole body, in differnet developmental stage') +
theme(axis.title.x = element_blank(),
axis.text.x = element_blank(),
axis.ticks = element_blank(),
panel.border=element_rect(colour="black",size=1, fill = NA))+
theme(legend.position='right')+ ylim(0, 3)p_allFam_body_adult = data %>% dplyr::filter(body.part == "Body") %>%
dplyr::filter(Stage =="adult") %>%
# mutate_at("Family", ~replace_na(.,"0")) %>%
na.omit() %>%
ggplot(aes(y=Ploidy, x=Family, fill = Sex, lable = Stage)) +
geom_boxplot(outlier.shape = NA, coef=0 ) + theme_bw() + geom_jitter(width=0.1, size=2) +
#facet_wrap(~Stage ) +
ggtitle('Mite Ploidy in whole body, per family') +
theme(axis.text.x = element_text(angle = 45)) + theme(legend.position='right')+ ylim(0, 3)
p_fam_body_1_11_27_3 = data %>% dplyr::filter(body.part == "Body") %>%
dplyr::filter(Family %in% c("1", "11","27","3")) %>%
ggplot(aes(y=Ploidy, x=Stage_original, fill = Sex, lable = Stage_original)) +
geom_boxplot(outlier.shape = NA, coef=0 ) + theme_bw() + geom_jitter(width=0.1, size=2) +
facet_wrap(~Family , nrow = 1) + ggtitle('Mite Ploidy per family') +
theme(axis.text.x = element_text(angle = 45)) + theme(legend.position='none')+ ylim(0, 3)
p_fam_body_2_4 = data %>% dplyr::filter(body.part == "Body") %>%
dplyr::filter(Family %in% c("2","4")) %>%
ggplot(aes(y=Ploidy, x=Stage_original, fill = Sex, lable = Stage_original)) +
geom_boxplot(outlier.shape = NA, coef=0 ) + theme_bw() + geom_jitter(width=0.1, size=2) +
facet_wrap(~Family ) + ggtitle('Mite Ploidy per family') +
theme(axis.text.x = element_text(angle = 45)) + theme(legend.position='none')+ ylim(0, 3)
p_fam_body_5 = data %>% dplyr::filter(body.part == "Body") %>%
dplyr::filter(Family == "5") %>%
ggplot(aes(y=Ploidy, x=Stage_original, fill = Sex, lable = Stage_original)) +
geom_boxplot(outlier.shape = NA, coef=0 ) + theme_bw() + geom_jitter(width=0.1, size=2) +
facet_wrap(~Family ) + ggtitle('Mite Ploidy per family') +
theme(axis.text.x = element_text(angle = 45)) + theme(legend.position='none')+ ylim(0, 3)all developmental stages, males and females, whole body
all developmental stages, males and females, in different body parts
| ### Looking at specific families |
| the Ploidy of the male offspring varied, depends on the family |
| for some families ( 1, 11, 27 and 3) the females Ploidy was higher then males. while females looks diploid, males look haploid. |
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for other families ( 2 and 4) the females Ploidy was similar to that of the male offspring.
in one family (number 5) the male offspring Ploidy was mixed:
one males look diploid, and the other two look haploid.